{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q值包含两个信息，一个为所在的位置，另一个为所在位置的所采取的动作\n",
    "$$A^\\pi(s,a)=Q^\\pi(s,a)-V^\\pi(s)$$\n",
    "$$Q^\\pi(s,a)=V^\\pi(s)+A^\\pi(s,a)-\\frac{A^\\pi(s,a)}{N_A}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gym\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from IPython.display import clear_output\n",
    "from torch.nn.utils import clip_grad_norm_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, column, max_size, batch_size):\n",
    "        self.current_state = np.zeros((max_size, column), dtype=np.float32)\n",
    "        self.next_state = np.zeros((max_size, column), dtype=np.float32)\n",
    "        self.action = np.zeros(max_size, dtype=np.float32)\n",
    "        self.reward = np.zeros(max_size, dtype=np.float32)\n",
    "        self.done = np.zeros(max_size,dtype=np.float32)\n",
    "        self.max_size, self.batch_size = max_size, batch_size\n",
    "        self.size, self.current_index = 0, 0\n",
    "    \n",
    "    def store(self, current_state, action, next_state, reward, done):\n",
    "        self.current_state[self.current_index] = current_state\n",
    "        self.action[self.current_index] = action\n",
    "        self.next_state[self.current_index] = next_state\n",
    "        self.reward[self.current_index] = reward\n",
    "        self.done[self.current_index] = done\n",
    "        self.current_index = (self.current_index + 1) % self.max_size\n",
    "        self.size = min(self.size + 1, self.max_size)\n",
    "    \n",
    "    def sample_batch(self):\n",
    "        ptr = np.random.choice(self.size, self.batch_size)\n",
    "        return dict(current_state=self.current_state[ptr],\n",
    "                    next_state=self.next_state[ptr],\n",
    "                    action=self.action[ptr],\n",
    "                    reward=self.reward[ptr],\n",
    "                    done=self.done[ptr]\n",
    "        )\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(nn.Module):\n",
    "    def __init__(self, in_dim, out_dim):\n",
    "        super(Network, self).__init__()\n",
    "        \n",
    "        self.common_layers = nn.Sequential(\n",
    "            nn.Linear(in_dim, 128),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "        self.advantage_layers = nn.Sequential(\n",
    "            nn.Linear(128, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, out_dim)\n",
    "        )\n",
    "        self.value_layers = nn.Sequential(\n",
    "            nn.Linear(128, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 1)\n",
    "        )\n",
    "    def forward(self,x):\n",
    "        common = self.common_layers(x)\n",
    "        advantage = self.advantage_layers(common)\n",
    "        value = self.value_layers(common)\n",
    "        Q = value + advantage - advantage.mean(0,keepdim=True)\n",
    "        return Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f85ececc0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_epsilon = 0.05\n",
    "max_epsilon = 1\n",
    "epsilon_decay = 60\n",
    "epsilon_episode = lambda episode : min_epsilon + np.exp(-episode / epsilon_decay)*0.95\n",
    "plt.plot([epsilon_episode(x) for x in range(600)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('CartPole-v1')\n",
    "\n",
    "seed = 777\n",
    "\n",
    "def seed_torch(seed):\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.backends.cudnn.enabled:\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "        torch.backends.cudnn.deterministic = True\n",
    "\n",
    "np.random.seed(seed)\n",
    "seed_torch(seed)\n",
    "env.seed(seed)        \n",
    "\n",
    "batch_size = 32\n",
    "max_size = 1000\n",
    "memory = ReplayBuffer(env.observation_space.shape[0], max_size, batch_size)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "network = Network(env.observation_space.shape[0],env.action_space.n).to(device)\n",
    "target_network = Network(env.observation_space.shape[0],env.action_space.n).to(device)\n",
    "target_network.load_state_dict(network.state_dict())\n",
    "target_network.eval()#把train设置为false，默认为true。可通过target_network.train()将train设置回true\n",
    "\n",
    "optimizer = optim.Adam(network.parameters(), lr=1e-2)\n",
    "\n",
    "gamma = 0.99\n",
    "target_update = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_action(episode, state):\n",
    "    if np.random.random_sample() > epsilon_episode(episode):\n",
    "        selected_action = network(torch.FloatTensor(state).to(device)).argmax().detach().cpu().numpy()\n",
    "    else:\n",
    "        selected_action = env.action_space.sample()\n",
    "    return selected_action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    samples = memory.sample_batch()\n",
    "    state = torch.FloatTensor(samples[\"current_state\"]).to(device).to(device)\n",
    "    next_state = torch.FloatTensor(samples[\"next_state\"]).to(device)\n",
    "    action = torch.LongTensor(samples[\"action\"].reshape(-1, 1)).to(device)#LongTensor\n",
    "    reward = torch.FloatTensor(samples[\"reward\"].reshape(-1, 1)).to(device)\n",
    "    done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
    "    \n",
    "    #main changes\n",
    "    current_Q_value = network(state).gather(1, action)\n",
    "    next_action = network(next_state).argmax(1, keepdim=True)\n",
    "    next_Q_value = target_network(next_state).gather(1,next_action).detach()\n",
    "    \n",
    "    target = (reward + gamma*next_Q_value*(1 - done)).to(device)\n",
    "    loss = ((target - current_Q_value).pow(2)).mean()\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    clip_grad_norm_(network.parameters(),1.0,norm_type=1) # Gradient clipping(增加稳定性)\n",
    "    optimizer.step()\n",
    "    return loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_reward(frame_idx, rewards, losses):\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(18,10))\n",
    "    plt.subplot(211)\n",
    "    plt.title('frame %s. mean_reward: %s'%(frame_idx, np.mean(rewards[-10:])))\n",
    "    plt.plot(rewards)\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('reward')\n",
    "    plt.subplot(212)\n",
    "    plt.plot(losses)\n",
    "    plt.ylabel('loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_rewards = []\n",
    "losses = []\n",
    "update_count = 0\n",
    "\n",
    "for episode in range(600):\n",
    "    state, rewards = env.reset(), 0\n",
    "    for i in range(10000):\n",
    "        action = select_action(episode, state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        memory.store(state, action, next_state, reward, done)\n",
    "        state = next_state\n",
    "        rewards += reward\n",
    "        if done:\n",
    "            break\n",
    "        if len(memory) > batch_size:\n",
    "            loss = train()\n",
    "            update_count += 1\n",
    "            losses.append(loss)\n",
    "            if update_count % target_update == 0:\n",
    "                target_network.load_state_dict(network.state_dict())     \n",
    "    all_rewards.append(rewards)\n",
    "    plot_reward(episode,all_rewards,losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 发现根本无法收敛哎"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
